1. Field of the Invention
This invention relates in general to data processing techniques for monitoring the performance of computer software and systems, and in particular to relational data base techniques, such as SQL engines and data servers, for collecting, monitoring and comparing performance data from computers in a network.
2. Description of the Prior Art
In conventional computer performance monitoring applications, such as the OMEGAMON system from CANDLE CORPORATION of Santa Monica, Calif., the monitoring application generates a request for data, such as "How busy is the CPU?". This request is sent by the monitoring application to the subsystem having such information via the network transport system. The subsystem returns the information requested to the monitoring application which then processes the data as required. Monitoring data is often processed by predicate logic to compare the data against a predetermined threshold. Such comparisons are typically performed by rule based testing.
The systems to be monitored often include complex mainframe based computer networks. The information to be monitored continuously becomes more complicated so that there are enormous amounts of information to be analyzed. In order to reduce the amount of data to be reviewed by the system operators, some techniques have been developed to further filter the data before review by the operator, one example of which is the display by exception technique of the OMEGAVIEW system referenced above. In that application, once the data has been collected, the internal logic of the OMEGAVIEW system displays data to the human operator in accordance with a predicate logic test. The data that has been retrieved is compared to a predetermined predicate or threshold level and is displayed to the operator if and only if the data exceeds the predicate or threshold.
As the computer network systems to be monitored grow in size and complexity, the data to be monitored and tested grow the same way. What are needed are improvements in the structure of database systems and monitoring applications to reduce the substantial computational time, and other overhead requirements, of conventional monitoring applications.